Evaluating Self-Supervised Learning Methods for Downstream Classification of Neoplasia in Barrett’s Esophagus

Abstract

A major problem in applying machine learning for the medical domain is the scarcity of labeled data, which results in the demand for methods that enable high-quality models trained with little to no labels. Self-supervised learning methods present a plausible solution to this problem, enabling the use of large sets of unlabeled data for model pretraining. In this study, multiple of these methods and training strategies are employed on a large dataset of endoscopic images from the gastrointestinal tract (GastroNet). The suitability of these methods is assessed for an intra-domain downstream classification task on a small endoscopic dataset, involving neoplasia in Barrett’s esophagus. The classification performances are compared against pretraining on ImageNet and training from scratch. This yields promising results for domain-specific self-supervised methods, where super-resolution outperforms pretraining on ImageNet with a mean classification accuracy of 83.8% (cf. 79.2%). This implies that the large amounts of unlabeled data in hospitals could be employed in combination with self-supervised learning methods to improve models for downstream tasks.